Machine learning identifies stroke features between species

Vega, Salvador Castaneda; Katiyar, Prateek; Russo, Francesca Maria; Patzwaldt, Kristin; Schnabel, Luisa; Mathes, Sarah; Hempel, Johann-Martin; Kohlhofer, Ursula; González-Menéndez, Irene; Quintanilla-Martı́nez, Leticia; Ziemann, Ulf; Fougère, Christian la; Ernemann, Ulrike; Pichler, Bernd J.; Disselhorst, Jonathan A.; Poli, Sven

doi:10.7150/thno.51887

Cited by 15 publications

(28 citation statements)

References 4 publications

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“…Eventually, our SA is translationally utilizing machine learning -enabled methodology from 3D human MRI-or CT-imaging data [16][17][18][19][20][21] on 2D tissue imaging data, reaching -at least-similar detection efficiency. 16,17 Recently published relevant MRI-data for rodents achieve results directly comparable to those of our SA for goodstained sections (Dice of 0.86 and FPR of 0.04) 59 .…”

Section: Discussionsupporting

confidence: 80%

“…In practice, the aforementioned methods have not achieved a wide appreciation and applicability by stroke laboratories, probably due to falsely detection of normal white-matter as lesion, significant inaccuracies upon poor processing and staining, and inability to correct falsely detected areas by the user. Although MRI-imaging 58,59 could theoretically compensate for stroke volumetry on sections, this is practically not widely applicable due to refractory high costs. Eventually, StrokeAnalyst addresses all these problems on tissue-based infarct-analysis by outlier detection and machine learning on a process that partially reflects the way human brain also works during manual lesion-analysis.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning based analysis of stroke lesions on mouse tissue sections

Damigos

Zacharaki

Zerva

et al. 2022

J Cereb Blood Flow Metab

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An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named “StrokeAnalyst”, which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2–2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Machine learning based analysis of stroke lesions on mouse tissue sections

Damigos

Zacharaki

Zerva

et al. 2022

J Cereb Blood Flow Metab

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“…Therefore, a prognosis model that predicts mid-term/short-term outcomes established by machine learning algorithm in stroke field began to gain attention (Heo et al, 2019;Brugnara et al, 2020;Liu et al, 2021). Furthermore, machine learning algorithm is able to deal with a huge number of complex variables and provides specific numerical values of different predictors (Deng et al, 2018;He et al, 2020;Castaneda-Vega et al, 2021). Among some widely used algorithms, light gradient boosting machine (LightGBM) is a classification model based on decision tree algorithm, with many advantages such as fast training speed, low memory consumption, high accuracy, and the ability to rapidly process massive data (Zhan et al, 2018;Chen et al, 2019;Shaker et al, 2021;Song et al, 2021;Liao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke

Zhu²,

Zhou³

et al. 2022

Front. Aging Neurosci.

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ObjectiveTo develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model.MethodsA total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data.ResultsA total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes.ConclusionPresurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.

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“…These characteristics make the random forest classifier an appropriate choice for gene expression datasets [ 22 ]. Furthermore, with the development of the next-generation sequencing (NGS), random forest has already been used extensively in the biomedical field, such as neurology [ 23 ], cancer classification and even protein–protein interaction sites prediction [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

Zhang

Liu

et al. 2021

J Transl Med

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Background The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative. Methods In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein–protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. Results The gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). Conclusions Our study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

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Machine learning identifies stroke features between species

Cited by 15 publications

References 4 publications

Machine learning based analysis of stroke lesions on mouse tissue sections

Machine learning based analysis of stroke lesions on mouse tissue sections

A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke

Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

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